system

Abstract

The system according to the embodiment aims to enable a user to efficiently obtain store information and coupon information in the vicinity of the user's current location and to select the most suitable coupon. [Solution] A system according to an embodiment includes an acquisition unit, a provision unit, a selection unit, and a real-time provision unit. The acquisition unit acquires GPS location information. The provision unit provides store information or coupon information based on the location information acquired by the acquisition unit. The selection unit selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit. The real-time provision unit updates the information provided by the provision unit in real time.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult for users to efficiently obtain store information and coupon information in the vicinity of their current location and to select the most suitable coupon.

[0005] The system according to the embodiment aims to enable a user to efficiently obtain store information and coupon information in the vicinity of the user's current location and to select the most suitable coupon. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a provision unit, a selection unit, and a real-time provision unit. The acquisition unit acquires GPS location information. The provision unit provides store information or coupon information based on the location information acquired by the acquisition unit. The selection unit selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit. The real-time provision unit updates the information provided by the provision unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to efficiently obtain store information and coupon information in the vicinity of the current location and select the most suitable coupon. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An information provision system according to an embodiment of the present invention utilizes GPS and a generation AI to provide real-time information about nearby stores and discount coupons. This information provision system acquires a user's current location, and the generation AI provides real-time information about nearby stores and discount coupons. Furthermore, when paying at a store, the system suggests coupons that can be used at that store and automatically selects the coupon with the highest discount rate. This mechanism allows users to always obtain the latest discount information and promotes the use of electronic payment systems. For example, when a user is at a shopping mall, coupon information for nearby restaurants and cafes is displayed. Furthermore, when paying at a store, the system suggests coupons that can be used at that store and automatically selects the coupon with the highest discount rate. For example, after a user finishes eating at a restaurant, the most discounted coupon is automatically applied at the time of payment. This mechanism allows users to always obtain the latest discount information and promotes the use of electronic payment systems. Furthermore, because the generation AI provides coupon information, users can easily use the most appropriate coupon. For example, when a user purchases coffee at a cafe, the generation AI provides coupon information for that cafe, and the most discounted coupon is automatically applied at the time of payment. This allows users to enjoy shopping at great prices and promotes the use of electronic payment systems. This service not only improves user convenience but also promotes the use of electronic payment systems. For example, when a user shops at a shopping mall, the AI ​​can provide information about nearby stores and coupons, encouraging the user to use the electronic payment system at more stores. Furthermore, because the service provides information in real time based on the user's location, the user can always access the latest deals. For example, when the user moves to a new location, the AI ​​can provide coupon information appropriate to that location. In this way, utilizing GPS and AI can improve user convenience and promote the use of electronic payment systems. This allows the information provision system to provide real-time deals based on the user's current location and automatically select the most appropriate coupon at the time of payment, thereby improving user convenience.

[0029] An information provision system according to an embodiment includes an acquisition unit, a provision unit, a selection unit, and a real-time provision unit. The acquisition unit acquires a user's current location using GPS. For example, the acquisition unit acquires the current location using a GPS module installed in the user's smartphone. The acquisition unit can also periodically update the user's current location. For example, the acquisition unit acquires GPS data at regular intervals and updates the current location. The provision unit uses a generation AI to provide store information and coupon information based on the location information acquired by the acquisition unit. For example, the generation AI collects store information around the user's current location and generates coupon information. The provision unit can also analyze the user's past behavior history and provide optimal coupon information. For example, the provision unit prioritizes providing coupon information for stores the user has visited in the past. The selection unit automatically selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit. For example, the selection unit compares coupon discount rates and terms of use and selects the most advantageous coupon. The selection unit can also refer to the user's past coupon usage history to select the optimal coupon. For example, the selection unit selects the most appropriate coupon based on the type and frequency of coupons used by the user in the past. The real-time provision unit updates the information provided by the provision unit in real time. For example, the real-time provision unit updates store information and coupon information every time the user's location information changes. The real-time provision unit can also adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the real-time provision unit provides information frequently when the user is shopping and reduces the frequency of information provision when the user is eating at a restaurant. In this way, the information provision system according to the embodiment can improve user convenience by providing advantageous information in real time based on the user's current location and automatically selecting the most appropriate coupon at the time of payment.

[0030] The providing unit can provide store information and coupon information using a generation AI. The generation AI generates store information and coupon information using, for example, natural language generation technology. For example, the generation AI collects information about nearby stores based on the user's current location and generates coupon information. The generation AI can also analyze the user's past behavior history and provide optimal coupon information. For example, the generation AI prioritizes providing coupon information for stores the user has visited in the past. Furthermore, the generation AI can adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the generation AI provides information frequently when the user is shopping and provides information less frequently when the user is eating at a restaurant. This improves the accuracy and relevance of the information provided by using the generation AI.

[0031] The selection unit can automatically select the coupon with the highest discount rate at the time of payment. The selection unit, for example, compares the discount rates and terms of use of coupons and selects the most advantageous coupon. For example, the selection unit automatically selects the coupon with the highest discount rate from among multiple coupons. The selection unit can also refer to the user's past coupon usage history to select the most appropriate coupon. For example, the selection unit selects the most appropriate coupon based on the type and frequency of coupons used by the user in the past. This allows the user to automatically use the most advantageous coupon.

[0032] The real-time providing unit can update information in real time based on the user's location information. For example, the real-time providing unit updates store information and coupon information every time the user's location information changes. For example, when the user moves to a new location, the real-time providing unit provides store information and coupon information appropriate to that location. The real-time providing unit can also adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the real-time providing unit provides information frequently when the user is shopping and reduces the frequency of information provision when the user is eating at a restaurant. This allows the user to always obtain the latest information.

[0033] The providing unit can analyze the user's history and provide appropriate coupon information. The providing unit can analyze, for example, the user's past purchase history and browsing history and provide optimal coupon information. For example, the providing unit can preferentially provide coupon information for products the user has previously purchased. The providing unit can also provide highly relevant coupon information based on the user's past behavior history. For example, the providing unit can preferentially provide coupon information for stores the user has previously visited. This makes it possible to provide more relevant coupon information based on the user's past behavior.

[0034] The information providing system further includes an update unit that periodically updates the coupon information. The update unit updates the coupon information daily, for example. For example, the update unit collects new coupon information every day and provides it to the user. The update unit can also update the coupon information weekly. For example, the update unit collects new coupon information every week and provides it to the user. The update unit can also update the coupon information monthly. For example, the update unit collects new coupon information every month and provides it to the user. This ensures that the coupon information is always kept up to date.

[0035] The acquisition unit can analyze the user's past movement history and select an optimal acquisition method. For example, the acquisition unit prioritizes locations that the user has frequently visited in the past as targets for acquiring location information. For example, the acquisition unit analyzes the user's past movement history and prioritizes locations that the user has frequently visited as targets for acquiring location information. The acquisition unit can also analyze the user's movement pattern and acquire location information during a specific time period. For example, the acquisition unit analyzes the user's movement pattern and acquires location information during a specific time period. Furthermore, the acquisition unit can select a timing for acquiring location information to avoid congestion based on the user's past movement history. For example, the acquisition unit analyzes the user's past movement history and selects a timing for acquiring location information to avoid congestion. This makes it possible to efficiently acquire location information based on the user's past movement history.

[0036] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is shopping, the acquisition unit prioritizes acquiring shopping-related location information. For example, the acquisition unit analyzes the user's current activity status and determines that the user is shopping, and prioritizes acquiring shopping-related location information. Furthermore, if the user is in a restaurant, the acquisition unit can prioritize acquiring restaurant-related location information. For example, the acquisition unit analyzes the user's current activity status and determines that the user is in a restaurant, and prioritizes acquiring restaurant-related location information. Furthermore, if the user is in a tourist destination, the acquisition unit can prioritize acquiring tourist spot-related location information. For example, the acquisition unit analyzes the user's current activity status and determines that the user is in a tourist destination, and prioritizes acquiring tourist spot-related location information. This makes it possible to acquire location information according to the user's current activity status and areas of interest.

[0037] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. For example, if the user is in an urban area, the acquisition unit prioritizes acquiring store information for the urban area. For example, if the acquisition unit analyzes the user's geographical location information and determines that the user is in an urban area, it prioritizes acquiring store information for the urban area. Furthermore, if the user is in a suburban area, the acquisition unit can prioritize acquiring store information for the suburban area. For example, if the acquisition unit analyzes the user's geographical location information and determines that the user is in a suburban area, it prioritizes acquiring store information for the suburban area. Furthermore, if the user is in a tourist destination, the acquisition unit can prioritize acquiring store information for the tourist destination. For example, if the acquisition unit analyzes the user's geographical location information and determines that the user is in a tourist destination, it prioritizes acquiring store information for the tourist destination. This makes it possible to acquire highly relevant information based on the user's geographical location information.

[0038] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit prioritizes acquiring information about the area around a location where the user has checked in on social media. For example, the acquisition unit analyzes the user's social media activity and prioritizes acquiring information about the area around the location where the user has checked in. The acquisition unit can also prioritize acquiring information about stores that the user follows on social media. For example, the acquisition unit analyzes the user's social media activity and prioritizes acquiring information about the stores that the user follows. Furthermore, the acquisition unit can also prioritize acquiring information about the area around an event in which the user has shown interest on social media. For example, the acquisition unit analyzes the user's social media activity and prioritizes acquiring information about the area around an event in which the user has shown interest. This makes it possible to acquire related information based on the user's social media activity.

[0039] The providing unit can adjust the level of detail of the provided store information and coupon information based on the importance of the store information and coupon information when providing the information. For example, the providing unit provides detailed information for coupon information with high importance. For example, the providing unit provides detailed information when the coupon discount rate or usage conditions are high. The providing unit can also provide brief information for store information with low importance. For example, the providing unit provides brief information when the coupon discount rate or usage conditions are low. Furthermore, the providing unit can also provide information with a moderate level of detail for information with a medium level of importance. For example, the providing unit provides information with a moderate level of detail when the coupon discount rate or usage conditions are medium. This makes it possible to adjust the level of detail of the provided store information and coupon information based on the importance of the store information and coupon information.

[0040] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies an algorithm to restaurant information that provides detailed information including menus and reviews. For example, when providing restaurant information, the providing unit provides detailed information including menus and reviews. The providing unit can also apply an algorithm to shopping information that provides information including product images and prices. For example, when providing shopping information, the providing unit provides information including product images and prices. The providing unit can also apply an algorithm to tourist information that provides photos of tourist spots and access information. For example, when providing tourist information, the providing unit provides photos of tourist spots and access information. This makes it possible to apply an appropriate providing algorithm depending on the category of information.

[0041] When providing the information, the providing unit can determine the priority of the information based on the user's past usage history. For example, the providing unit can prioritize providing information about stores that the user has frequently used in the past. For example, the providing unit can analyze the user's past usage history and prioritize providing information about stores that the user has frequently used. The providing unit can also prioritize providing information about coupons that the user has used in the past. For example, the providing unit can analyze the user's past coupon usage history and prioritize providing information about coupons that the user has frequently used. Furthermore, the providing unit can also prioritize providing highly relevant information from the user's past usage history. For example, the providing unit can analyze the user's past usage history and prioritize providing highly relevant information. This makes it possible to determine the priority of the information based on the user's past usage history.

[0042] The providing unit can adjust the order of information based on the relevance of the user when providing the information. For example, the providing unit preferentially displays information about stores close to the user's current location. For example, the providing unit analyzes the user's current location and preferentially displays information about nearby stores. The providing unit can also preferentially display information about categories in which the user has shown interest. For example, the providing unit analyzes the user's areas of interest and preferentially displays information about categories in which the user has shown interest. Furthermore, the providing unit can also preferentially display highly relevant information based on the user's past usage history. For example, the providing unit analyzes the user's past usage history and preferentially displays highly relevant information. This makes it possible to adjust the order of information based on the user's relevance.

[0043] When selecting, the selection unit can select an appropriate coupon by taking into consideration the coupon's expiration date and usage conditions. The selection unit, for example, prioritizes the selection of coupons that are close to their expiration date. For example, the selection unit analyzes the coupon's expiration date and prioritizes the selection of coupons that are close to their expiration date. The selection unit can also prioritize the selection of coupons with simple usage conditions. For example, the selection unit analyzes the coupon's usage conditions and prioritizes the selection of coupons with simple conditions. The selection unit can also prioritize the selection of coupons whose usage conditions are suitable for the user. For example, the selection unit analyzes the coupon's usage conditions and prioritizes the selection of coupons that are suitable for the user. This makes it possible to select the optimal coupon by taking into consideration the coupon's expiration date and usage conditions.

[0044] When making a selection, the selection unit can improve the accuracy of the selection by referring to the user's past coupon usage history. The selection unit, for example, selects an optimal coupon based on the types of coupons the user has used in the past. For example, the selection unit analyzes the user's past coupon usage history and selects an optimal coupon based on the types of coupons used. The selection unit can also prioritize selecting coupons that have been used frequently from the user's past coupon usage history. For example, the selection unit analyzes the user's past coupon usage history and selects coupons that have been used frequently. Furthermore, the selection unit can analyze the user's past coupon usage history and select the most effective coupon. For example, the selection unit analyzes the user's past coupon usage history and selects the most effective coupon. This makes it possible to select an optimal coupon based on the user's past coupon usage history.

[0045] When selecting, the selection unit can select an appropriate coupon by taking into consideration the user's geographical location information. For example, the selection unit preferentially selects coupons for stores close to the user's current location. For example, the selection unit analyzes the user's geographical location information and preferentially selects coupons for stores close to the user's current location. Furthermore, if the user is in a specific area, the selection unit can preferentially select coupons for that area. For example, the selection unit analyzes the user's geographical location information and, if it determines that the user is in a specific area, preferentially selects coupons for that area. Furthermore, if the user is traveling, the selection unit can preferentially select coupons for the user's destination. For example, the selection unit analyzes the user's geographical location information and, if it determines that the user is traveling, preferentially selects coupons for the user's destination. This makes it possible to select optimal coupons based on the user's geographical location information.

[0046] At the time of selection, the selection unit can analyze the user's social media activity to select a coupon. For example, the selection unit preferentially selects coupons for stores that the user follows on social media. For example, the selection unit analyzes the user's social media activity and preferentially selects coupons for stores that the user follows. The selection unit can also preferentially select coupons for products that the user has shown interest in on social media. For example, the selection unit analyzes the user's social media activity and preferentially selects coupons for products that the user has shown interest in. Furthermore, the selection unit can also preferentially select coupons that are highly relevant to the user's social media activity. For example, the selection unit analyzes the user's social media activity and preferentially selects coupons that are highly relevant. This makes it possible to select optimal coupons based on the user's social media activity.

[0047] The real-time providing unit can adjust the frequency of information provision in consideration of the user's current activity status when providing information in real time. For example, if the user is shopping, the real-time providing unit provides information frequently. For example, the real-time providing unit analyzes the user's current activity status and determines that the user is shopping, and provides information frequently. The real-time providing unit can also reduce the frequency of information provision if the user is eating at a restaurant. For example, the real-time providing unit analyzes the user's current activity status and determines that the user is eating at a restaurant, and reduces the frequency of information provision. Furthermore, the real-time providing unit can also provide information in real time if the user is on the move. For example, the real-time providing unit analyzes the user's current activity status and determines that the user is on the move, and provides information in real time. This makes it possible to adjust the frequency of information provision according to the user's current activity status.

[0048] The real-time providing unit can provide optimal information by referring to the user's past usage history during real-time provision. The real-time providing unit, for example, prioritizes providing information about stores that the user has used in the past. For example, the real-time providing unit analyzes the user's past usage history and prioritizes providing information about stores that the user has used in the past. The real-time providing unit can also prioritize providing highly relevant information from the user's past usage history. For example, the real-time providing unit analyzes the user's past usage history and prioritizes providing highly relevant information. Furthermore, the real-time providing unit can also analyze the user's past usage history and provide the most effective information. For example, the real-time providing unit analyzes the user's past usage history and provides the most effective information. This makes it possible to provide optimal information based on the user's past usage history.

[0049] The real-time providing unit can provide optimal information in real time, taking into consideration the user's geographical location information. For example, if the user is in an urban area, the real-time providing unit preferentially provides store information in the urban area. For example, if the real-time providing unit analyzes the user's geographical location information and determines that the user is in an urban area, it preferentially provides store information in the urban area. Furthermore, the real-time providing unit can also preferentially provide store information in the suburbs if the user is in a suburban area. For example, if the real-time providing unit analyzes the user's geographical location information and determines that the user is in a suburban area, it preferentially provides store information in the suburban area. Furthermore, the real-time providing unit can also preferentially provide information about tourist spots if the user is in a tourist destination. For example, if the real-time providing unit analyzes the user's geographical location information and determines that the user is in a tourist destination, it preferentially provides information about tourist spots. This makes it possible to provide optimal information based on the user's geographical location information.

[0050] The real-time providing unit can analyze the user's social media activity and provide information during real-time provision. For example, the real-time providing unit prioritizes providing information about the area around a location where the user has checked in on social media. For example, the real-time providing unit analyzes the user's social media activity and prioritizes providing information about the area around the location where the user has checked in. The real-time providing unit can also prioritize providing information about stores that the user follows on social media. For example, the real-time providing unit analyzes the user's social media activity and prioritizes providing information about the stores that the user follows. Furthermore, the real-time providing unit can also prioritize providing information about events in which the user has shown interest on social media. For example, the real-time providing unit analyzes the user's social media activity and prioritizes providing information about events in which the user has shown interest. This makes it possible to provide optimal information based on the user's social media activity.

[0051] The update unit can optimize the update algorithm by referring to past coupon usage data during updating. The update unit, for example, updates optimal coupon information based on the types of coupons used by the user in the past. For example, the update unit analyzes the user's past coupon usage data and updates optimal coupon information based on the types of coupons used. The update unit can also prioritize updating frequently used coupon information based on the user's past coupon usage history. For example, the update unit analyzes the user's past coupon usage history and prioritizes updating frequently used coupon information. Furthermore, the update unit can analyze the user's past coupon usage history and update the most effective coupon information. For example, the update unit analyzes the user's past coupon usage history and updates the most effective coupon information. This makes it possible to optimize the update algorithm based on past coupon usage data.

[0052] When updating, the update unit can update information taking into consideration the expiration date and usage conditions of the coupon. For example, the update unit prioritizes updating coupon information that is close to its expiration date. For example, the update unit analyzes the expiration date of the coupon and prioritizes updating coupon information that is close to its expiration date. The update unit can also prioritize updating coupon information with simple usage conditions. For example, the update unit analyzes the usage conditions of the coupon and prioritizes updating coupon information with simple conditions. Furthermore, the update unit can also prioritize updating coupon information with usage conditions that are suitable for the user. For example, the update unit analyzes the usage conditions of the coupon and prioritizes updating coupon information that is suitable for the user. This makes it possible to update information based on the expiration date and usage conditions of the coupon.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The acquisition unit can analyze the user's past movement history and adjust the frequency of acquiring location information during a specific time period. For example, the acquisition unit can identify places that the user has frequently visited in the past and increase the frequency of acquiring location information during times when the user is at those places. In addition, if the user tends to move during a specific time period, the acquisition unit can also increase the frequency of acquiring location information during that time period. Furthermore, if the user tends to stay in a specific place during a specific time period, the acquisition unit can also reduce the frequency of acquiring location information during that time period. This allows for efficient acquisition of location information based on the user's past movement history.

[0055] The providing unit can analyze the user's social media activity and provide related store information and coupon information. For example, the providing unit can prioritize providing coupon information for stores that the user follows on social media. The providing unit can also provide coupon information for products that the user has shown interest in on social media. Furthermore, the providing unit can also provide information about the surrounding area of ​​a location where the user has checked in on social media. This makes it possible to provide highly relevant information based on the user's social media activity.

[0056] The real-time providing unit can analyze the user's current activity status and adjust the frequency of information provision. For example, the real-time providing unit can provide information frequently when the user is shopping. The real-time providing unit can also reduce the frequency of information provision when the user is eating at a restaurant. Furthermore, the real-time providing unit can also provide information in real time when the user is on the move. In this way, the frequency of information provision can be adjusted according to the user's current activity status.

[0057] The acquisition unit can adjust the timing of acquiring location information based on the user's current activity status and area of ​​interest. For example, when the user is shopping, the acquisition unit prioritizes acquiring shopping-related location information. When the user is in a restaurant, the acquisition unit can also prioritize acquiring restaurant-related location information. Furthermore, when the user is in a tourist spot, the acquisition unit can also prioritize acquiring tourist spot-related location information. This makes it possible to acquire location information according to the user's current activity status and area of ​​interest.

[0058] The providing unit can adjust the level of detail of the provided information based on the importance of the store information and coupon information when providing the information. For example, the providing unit can provide coupon information with high importance in detail. The providing unit can also provide store information with low importance in a concise manner. Furthermore, the providing unit can provide information with medium importance in an appropriate level of detail. This makes it possible to adjust the level of detail of the provided information based on the importance of the store information and coupon information.

[0059] When selecting a coupon, the selection unit can select an appropriate coupon by taking into consideration the coupon's expiration date and terms of use. For example, the selection unit can prioritize selecting coupons that are close to their expiration date. The selection unit can also prioritize selecting coupons with simple terms of use. Furthermore, the selection unit can also prioritize selecting coupons with terms of use that are suitable for the user. This allows the optimal coupon to be selected by taking into consideration the coupon's expiration date and terms of use.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The acquisition unit acquires the user's current location using GPS. For example, the acquisition unit acquires the current location using a GPS module installed in the user's smartphone. The acquisition unit can also periodically update the user's current location. For example, the acquisition unit acquires GPS data at regular intervals and updates the current location. Step 2: The providing unit uses the generation AI to provide store information and coupon information based on the location information acquired by the acquisition unit. For example, the generating AI collects store information around the user's current location and generates coupon information. The providing unit can also analyze the user's past behavior history and provide optimal coupon information. For example, the providing unit prioritizes providing coupon information for stores the user has visited in the past. Step 3: The selection unit automatically selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit. For example, the selection unit compares the discount rates and terms of use of coupons and selects the most advantageous coupon. The selection unit can also refer to the user's past coupon usage history to select the most appropriate coupon. For example, the selection unit selects the most appropriate coupon based on the types and frequency of coupons the user has used in the past. Step 4: The real-time providing unit updates the information provided by the providing unit in real time. For example, the real-time providing unit updates store information and coupon information every time the user's location information changes. The real-time providing unit can also adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the real-time providing unit provides information frequently when the user is shopping and reduces the frequency of information provision when the user is eating at a restaurant.

[0062] (Example 2) An information provision system according to an embodiment of the present invention utilizes GPS and a generation AI to provide real-time information about nearby stores and discount coupons. This information provision system acquires a user's current location, and the generation AI provides real-time information about nearby stores and discount coupons. Furthermore, when paying at a store, the system suggests coupons that can be used at that store and automatically selects the coupon with the highest discount rate. This mechanism allows users to always obtain the latest discount information and promotes the use of electronic payment systems. For example, when a user is at a shopping mall, coupon information for nearby restaurants and cafes is displayed. Furthermore, when paying at a store, the system suggests coupons that can be used at that store and automatically selects the coupon with the highest discount rate. For example, after a user finishes eating at a restaurant, the most discounted coupon is automatically applied at the time of payment. This mechanism allows users to always obtain the latest discount information and promotes the use of electronic payment systems. Furthermore, because the generation AI provides coupon information, users can easily use the most appropriate coupon. For example, when a user purchases coffee at a cafe, the generation AI provides coupon information for that cafe, and the most discounted coupon is automatically applied at the time of payment. This allows users to enjoy shopping at great prices and promotes the use of electronic payment systems. This service not only improves user convenience but also promotes the use of electronic payment systems. For example, when a user shops at a shopping mall, the AI ​​can provide information about nearby stores and coupons, encouraging the user to use the electronic payment system at more stores. Furthermore, because the service provides information in real time based on the user's location, the user can always access the latest deals. For example, when the user moves to a new location, the AI ​​can provide coupon information appropriate to that location. In this way, utilizing GPS and AI can improve user convenience and promote the use of electronic payment systems. This allows the information provision system to provide real-time deals based on the user's current location and automatically select the most appropriate coupon at the time of payment, thereby improving user convenience.

[0063] An information provision system according to an embodiment includes an acquisition unit, a provision unit, a selection unit, and a real-time provision unit. The acquisition unit acquires a user's current location using GPS. For example, the acquisition unit acquires the current location using a GPS module installed in the user's smartphone. The acquisition unit can also periodically update the user's current location. For example, the acquisition unit acquires GPS data at regular intervals and updates the current location. The provision unit uses a generation AI to provide store information and coupon information based on the location information acquired by the acquisition unit. For example, the generation AI collects store information around the user's current location and generates coupon information. The provision unit can also analyze the user's past behavior history and provide optimal coupon information. For example, the provision unit prioritizes providing coupon information for stores the user has visited in the past. The selection unit automatically selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit. For example, the selection unit compares coupon discount rates and terms of use and selects the most advantageous coupon. The selection unit can also refer to the user's past coupon usage history to select the optimal coupon. For example, the selection unit selects the most appropriate coupon based on the type and frequency of coupons used by the user in the past. The real-time provision unit updates the information provided by the provision unit in real time. For example, the real-time provision unit updates store information and coupon information every time the user's location information changes. The real-time provision unit can also adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the real-time provision unit provides information frequently when the user is shopping and reduces the frequency of information provision when the user is eating at a restaurant. In this way, the information provision system according to the embodiment can improve user convenience by providing advantageous information in real time based on the user's current location and automatically selecting the most appropriate coupon at the time of payment.

[0064] The providing unit can provide store information and coupon information using a generation AI. The generation AI generates store information and coupon information using, for example, natural language generation technology. For example, the generation AI collects information about nearby stores based on the user's current location and generates coupon information. The generation AI can also analyze the user's past behavior history and provide optimal coupon information. For example, the generation AI prioritizes providing coupon information for stores the user has visited in the past. Furthermore, the generation AI can adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the generation AI provides information frequently when the user is shopping and provides information less frequently when the user is eating at a restaurant. This improves the accuracy and relevance of the information provided by using the generation AI.

[0065] The selection unit can automatically select the coupon with the highest discount rate at the time of payment. The selection unit, for example, compares the discount rates and terms of use of coupons and selects the most advantageous coupon. For example, the selection unit automatically selects the coupon with the highest discount rate from among multiple coupons. The selection unit can also refer to the user's past coupon usage history to select the most appropriate coupon. For example, the selection unit selects the most appropriate coupon based on the type and frequency of coupons used by the user in the past. This allows the user to automatically use the most advantageous coupon.

[0066] The real-time providing unit can update information in real time based on the user's location information. For example, the real-time providing unit updates store information and coupon information every time the user's location information changes. For example, when the user moves to a new location, the real-time providing unit provides store information and coupon information appropriate to that location. The real-time providing unit can also adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the real-time providing unit provides information frequently when the user is shopping and reduces the frequency of information provision when the user is eating at a restaurant. This allows the user to always obtain the latest information.

[0067] The providing unit can analyze the user's history and provide appropriate coupon information. The providing unit can analyze, for example, the user's past purchase history and browsing history and provide optimal coupon information. For example, the providing unit can preferentially provide coupon information for products the user has previously purchased. The providing unit can also provide highly relevant coupon information based on the user's past behavior history. For example, the providing unit can preferentially provide coupon information for stores the user has previously visited. This makes it possible to provide more relevant coupon information based on the user's past behavior.

[0068] The information providing system further includes an update unit that periodically updates the coupon information. The update unit updates the coupon information daily, for example. For example, the update unit collects new coupon information every day and provides it to the user. The update unit can also update the coupon information weekly. For example, the update unit collects new coupon information every week and provides it to the user. The update unit can also update the coupon information monthly. For example, the update unit collects new coupon information every month and provides it to the user. This ensures that the coupon information is always kept up to date.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit reduces the frequency of acquiring location information and reduces notifications. For example, if the acquisition unit estimates the user's emotions and determines that the user is feeling stressed, it reduces the frequency of acquiring location information. Furthermore, if the user is relaxed, the acquisition unit can increase the frequency of acquiring location information and actively provide special offers. For example, if the acquisition unit estimates the user's emotions and determines that the user is relaxed, it increases the frequency of acquiring location information. Furthermore, if the user is in a hurry, the acquisition unit can acquire location information in real time and immediately provide special offers. For example, if the acquisition unit estimates the user's emotions and determines that the user is in a hurry, it acquires location information in real time. In this way, the timing of acquiring location information can be adjusted according to the user's emotions, thereby reducing the user's stress.

[0070] The acquisition unit can analyze the user's past movement history and select an optimal acquisition method. For example, the acquisition unit prioritizes locations that the user has frequently visited in the past as targets for acquiring location information. For example, the acquisition unit analyzes the user's past movement history and prioritizes locations that the user has frequently visited as targets for acquiring location information. The acquisition unit can also analyze the user's movement pattern and acquire location information during a specific time period. For example, the acquisition unit analyzes the user's movement pattern and acquires location information during a specific time period. Furthermore, the acquisition unit can select a timing for acquiring location information to avoid congestion based on the user's past movement history. For example, the acquisition unit analyzes the user's past movement history and selects a timing for acquiring location information to avoid congestion. This makes it possible to efficiently acquire location information based on the user's past movement history.

[0071] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is shopping, the acquisition unit prioritizes acquiring shopping-related location information. For example, the acquisition unit analyzes the user's current activity status and determines that the user is shopping, and prioritizes acquiring shopping-related location information. Furthermore, if the user is in a restaurant, the acquisition unit can prioritize acquiring restaurant-related location information. For example, the acquisition unit analyzes the user's current activity status and determines that the user is in a restaurant, and prioritizes acquiring restaurant-related location information. Furthermore, if the user is in a tourist destination, the acquisition unit can prioritize acquiring tourist spot-related location information. For example, the acquisition unit analyzes the user's current activity status and determines that the user is in a tourist destination, and prioritizes acquiring tourist spot-related location information. This makes it possible to acquire location information according to the user's current activity status and areas of interest.

[0072] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user's emotions. For example, if the user is excited, the acquisition unit prioritizes acquiring entertainment-related location information. For example, if the acquisition unit estimates the user's emotions and determines that the user is excited, it prioritizes acquiring entertainment-related location information. Furthermore, if the user is tired, the acquisition unit can prioritize acquiring location information of places where the user can relax. For example, if the acquisition unit estimates the user's emotions and determines that the user is tired, it prioritizes acquiring location information of places where the user can relax. Furthermore, if the user is hungry, the acquisition unit can prioritize acquiring location information of restaurants. For example, if the acquisition unit estimates the user's emotions and determines that the user is hungry, it prioritizes acquiring location information of restaurants. In this way, the priority of location information to be acquired can be determined according to the user's emotions.

[0073] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. For example, if the user is in an urban area, the acquisition unit prioritizes acquiring store information for the urban area. For example, if the acquisition unit analyzes the user's geographical location information and determines that the user is in an urban area, it prioritizes acquiring store information for the urban area. Furthermore, if the user is in a suburban area, the acquisition unit can prioritize acquiring store information for the suburban area. For example, if the acquisition unit analyzes the user's geographical location information and determines that the user is in a suburban area, it prioritizes acquiring store information for the suburban area. Furthermore, if the user is in a tourist destination, the acquisition unit can prioritize acquiring store information for the tourist destination. For example, if the acquisition unit analyzes the user's geographical location information and determines that the user is in a tourist destination, it prioritizes acquiring store information for the tourist destination. This makes it possible to acquire highly relevant information based on the user's geographical location information.

[0074] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit prioritizes acquiring information about the area around a location where the user has checked in on social media. For example, the acquisition unit analyzes the user's social media activity and prioritizes acquiring information about the area around the location where the user has checked in. The acquisition unit can also prioritize acquiring information about stores that the user follows on social media. For example, the acquisition unit analyzes the user's social media activity and prioritizes acquiring information about the stores that the user follows. Furthermore, the acquisition unit can also prioritize acquiring information about the area around an event in which the user has shown interest on social media. For example, the acquisition unit analyzes the user's social media activity and prioritizes acquiring information about the area around an event in which the user has shown interest. This makes it possible to acquire related information based on the user's social media activity.

[0075] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides information at a leisurely pace. For example, if the providing unit estimates the user's emotions and determines that the user is relaxed, it provides information at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can provide information quickly. For example, if the providing unit estimates the user's emotions and determines that the user is in a hurry, it provides information quickly. Furthermore, if the user is excited, the providing unit can provide information by adding a visually stimulating effect. For example, if the providing unit estimates the user's emotions and determines that the user is excited, it provides information by adding a visually stimulating effect. This makes it possible to adjust the method of providing information according to the user's emotions.

[0076] The providing unit can adjust the level of detail of the provided store information and coupon information based on the importance of the store information and coupon information when providing the information. For example, the providing unit provides detailed information for coupon information with high importance. For example, the providing unit provides detailed information when the coupon discount rate or usage conditions are high. The providing unit can also provide brief information for store information with low importance. For example, the providing unit provides brief information when the coupon discount rate or usage conditions are low. Furthermore, the providing unit can also provide information with a moderate level of detail for information with a medium level of importance. For example, the providing unit provides information with a moderate level of detail when the coupon discount rate or usage conditions are medium. This makes it possible to adjust the level of detail of the provided store information and coupon information based on the importance of the store information and coupon information.

[0077] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies an algorithm to restaurant information that provides detailed information including menus and reviews. For example, when providing restaurant information, the providing unit provides detailed information including menus and reviews. The providing unit can also apply an algorithm to shopping information that provides information including product images and prices. For example, when providing shopping information, the providing unit provides information including product images and prices. The providing unit can also apply an algorithm to tourist information that provides photos of tourist spots and access information. For example, when providing tourist information, the providing unit provides photos of tourist spots and access information. This makes it possible to apply an appropriate providing algorithm depending on the category of information.

[0078] The providing unit can estimate the user's emotions and adjust the frequency of information provision based on the estimated user's emotions. For example, the providing unit increases the frequency of information provision when the user is relaxed. For example, the providing unit estimates the user's emotions and determines that the user is relaxed, and increases the frequency of information provision. The providing unit can also decrease the frequency of information provision when the user is stressed. For example, the providing unit estimates the user's emotions and determines that the user is stressed, and decreases the frequency of information provision. Furthermore, the providing unit can also adjust the frequency of information provision in real time when the user is in a hurry. For example, the providing unit estimates the user's emotions and determines that the user is in a hurry, and adjusts the frequency of information provision in real time. This makes it possible to adjust the frequency of information provision according to the user's emotions.

[0079] When providing the information, the providing unit can determine the priority of the information based on the user's past usage history. For example, the providing unit can prioritize providing information about stores that the user has frequently used in the past. For example, the providing unit can analyze the user's past usage history and prioritize providing information about stores that the user has frequently used. The providing unit can also prioritize providing information about coupons that the user has used in the past. For example, the providing unit can analyze the user's past coupon usage history and prioritize providing information about coupons that the user has frequently used. Furthermore, the providing unit can also prioritize providing highly relevant information from the user's past usage history. For example, the providing unit can analyze the user's past usage history and prioritize providing highly relevant information. This makes it possible to determine the priority of the information based on the user's past usage history.

[0080] The providing unit can adjust the order of information based on the relevance of the user when providing the information. For example, the providing unit preferentially displays information about stores close to the user's current location. For example, the providing unit analyzes the user's current location and preferentially displays information about nearby stores. The providing unit can also preferentially display information about categories in which the user has shown interest. For example, the providing unit analyzes the user's areas of interest and preferentially displays information about categories in which the user has shown interest. Furthermore, the providing unit can also preferentially display highly relevant information based on the user's past usage history. For example, the providing unit analyzes the user's past usage history and preferentially displays highly relevant information. This makes it possible to adjust the order of information based on the user's relevance.

[0081] The selection unit can estimate the user's emotions and adjust coupon selection criteria based on the estimated user's emotions. For example, if the user is relaxed, the selection unit preferentially selects coupons with higher discount rates. For example, if the selection unit estimates the user's emotions and determines that the user is relaxed, it preferentially selects coupons with higher discount rates. The selection unit can also preferentially select coupons that can be used immediately if the user is in a hurry. For example, if the selection unit estimates the user's emotions and determines that the user is in a hurry, it preferentially selects coupons that can be used immediately. Furthermore, the selection unit can also preferentially select coupons with many perks if the user is excited. For example, if the selection unit estimates the user's emotions and determines that the user is excited, it preferentially selects coupons with many perks. This makes it possible to adjust the coupon selection criteria according to the user's emotions.

[0082] When selecting, the selection unit can select an appropriate coupon by taking into consideration the coupon's expiration date and usage conditions. The selection unit, for example, prioritizes the selection of coupons that are close to their expiration date. For example, the selection unit analyzes the coupon's expiration date and prioritizes the selection of coupons that are close to their expiration date. The selection unit can also prioritize the selection of coupons with simple usage conditions. For example, the selection unit analyzes the coupon's usage conditions and prioritizes the selection of coupons with simple conditions. The selection unit can also prioritize the selection of coupons whose usage conditions are suitable for the user. For example, the selection unit analyzes the coupon's usage conditions and prioritizes the selection of coupons that are suitable for the user. This makes it possible to select the optimal coupon by taking into consideration the coupon's expiration date and usage conditions.

[0083] When making a selection, the selection unit can improve the accuracy of the selection by referring to the user's past coupon usage history. The selection unit, for example, selects an optimal coupon based on the types of coupons the user has used in the past. For example, the selection unit analyzes the user's past coupon usage history and selects an optimal coupon based on the types of coupons used. The selection unit can also prioritize selecting coupons that have been used frequently from the user's past coupon usage history. For example, the selection unit analyzes the user's past coupon usage history and selects coupons that have been used frequently. Furthermore, the selection unit can analyze the user's past coupon usage history and select the most effective coupon. For example, the selection unit analyzes the user's past coupon usage history and selects the most effective coupon. This makes it possible to select an optimal coupon based on the user's past coupon usage history.

[0084] The selection unit can estimate the user's emotions and adjust the coupon display method based on the estimated user's emotions. For example, the selection unit displays detailed coupon information when the user is relaxed. For example, the selection unit estimates the user's emotions and determines that the user is relaxed, and displays detailed coupon information. The selection unit can also display concise coupon information when the user is in a hurry. For example, the selection unit estimates the user's emotions and determines that the user is in a hurry, and displays concise coupon information. Furthermore, the selection unit can also display visually appealing coupon information when the user is excited. For example, the selection unit estimates the user's emotions and determines that the user is excited, and displays visually appealing coupon information. This makes it possible to adjust the coupon display method according to the user's emotions.

[0085] When selecting, the selection unit can select an appropriate coupon by taking into consideration the user's geographical location information. For example, the selection unit preferentially selects coupons for stores close to the user's current location. For example, the selection unit analyzes the user's geographical location information and preferentially selects coupons for stores close to the user's current location. Furthermore, if the user is in a specific area, the selection unit can preferentially select coupons for that area. For example, the selection unit analyzes the user's geographical location information and, if it determines that the user is in a specific area, preferentially selects coupons for that area. Furthermore, if the user is traveling, the selection unit can preferentially select coupons for the user's destination. For example, the selection unit analyzes the user's geographical location information and, if it determines that the user is traveling, preferentially selects coupons for the user's destination. This makes it possible to select optimal coupons based on the user's geographical location information.

[0086] At the time of selection, the selection unit can analyze the user's social media activity to select a coupon. For example, the selection unit preferentially selects coupons for stores that the user follows on social media. For example, the selection unit analyzes the user's social media activity and preferentially selects coupons for stores that the user follows. The selection unit can also preferentially select coupons for products that the user has shown interest in on social media. For example, the selection unit analyzes the user's social media activity and preferentially selects coupons for products that the user has shown interest in. Furthermore, the selection unit can also preferentially select coupons that are highly relevant to the user's social media activity. For example, the selection unit analyzes the user's social media activity and preferentially selects coupons that are highly relevant. This makes it possible to select optimal coupons based on the user's social media activity.

[0087] The real-time providing unit can estimate the user's emotions and adjust the display method of the real-time information based on the estimated user's emotions. For example, the real-time providing unit displays detailed real-time information when the user is relaxed. For example, the real-time providing unit estimates the user's emotions and determines that the user is relaxed, and displays detailed real-time information. The real-time providing unit can also display concise real-time information when the user is in a hurry. For example, the real-time providing unit estimates the user's emotions and determines that the user is in a hurry, and displays concise real-time information. Furthermore, the real-time providing unit can also display visually appealing real-time information when the user is excited. For example, the real-time providing unit estimates the user's emotions and determines that the user is excited, and displays visually appealing real-time information. This makes it possible to adjust the display method of the real-time information according to the user's emotions.

[0088] The real-time providing unit can adjust the frequency of information provision in consideration of the user's current activity status when providing information in real time. For example, if the user is shopping, the real-time providing unit provides information frequently. For example, the real-time providing unit analyzes the user's current activity status and determines that the user is shopping, and provides information frequently. The real-time providing unit can also reduce the frequency of information provision if the user is eating at a restaurant. For example, the real-time providing unit analyzes the user's current activity status and determines that the user is eating at a restaurant, and reduces the frequency of information provision. Furthermore, the real-time providing unit can also provide information in real time if the user is on the move. For example, the real-time providing unit analyzes the user's current activity status and determines that the user is on the move, and provides information in real time. This makes it possible to adjust the frequency of information provision according to the user's current activity status.

[0089] The real-time providing unit can provide optimal information by referring to the user's past usage history during real-time provision. The real-time providing unit, for example, prioritizes providing information about stores that the user has used in the past. For example, the real-time providing unit analyzes the user's past usage history and prioritizes providing information about stores that the user has used in the past. The real-time providing unit can also prioritize providing highly relevant information from the user's past usage history. For example, the real-time providing unit analyzes the user's past usage history and prioritizes providing highly relevant information. Furthermore, the real-time providing unit can also analyze the user's past usage history and provide the most effective information. For example, the real-time providing unit analyzes the user's past usage history and provides the most effective information. This makes it possible to provide optimal information based on the user's past usage history.

[0090] The real-time providing unit can estimate the user's emotions and determine the priority of real-time information based on the estimated user's emotions. For example, if the user is relaxed, the real-time providing unit can provide entertainment-related information preferentially. For example, if the real-time providing unit estimates the user's emotions and determines that the user is relaxed, the real-time providing unit can provide entertainment-related information preferentially. Furthermore, if the user is in a hurry, the real-time providing unit can provide traffic information or emergency information preferentially. For example, if the real-time providing unit estimates the user's emotions and determines that the user is in a hurry, the real-time providing unit can provide traffic information or emergency information preferentially. Furthermore, if the user is excited, the real-time providing unit can provide event information or special offer information preferentially. For example, if the real-time providing unit estimates the user's emotions and determines that the user is excited, the real-time providing unit can provide event information or special offer information preferentially. In this way, the priority of real-time information can be determined according to the user's emotions.

[0091] The real-time providing unit can provide optimal information in real time, taking into consideration the user's geographical location information. For example, if the user is in an urban area, the real-time providing unit preferentially provides store information in the urban area. For example, if the real-time providing unit analyzes the user's geographical location information and determines that the user is in an urban area, it preferentially provides store information in the urban area. Furthermore, the real-time providing unit can also preferentially provide store information in the suburbs if the user is in a suburban area. For example, if the real-time providing unit analyzes the user's geographical location information and determines that the user is in a suburban area, it preferentially provides store information in the suburban area. Furthermore, the real-time providing unit can also preferentially provide information about tourist spots if the user is in a tourist destination. For example, if the real-time providing unit analyzes the user's geographical location information and determines that the user is in a tourist destination, it preferentially provides information about tourist spots. This makes it possible to provide optimal information based on the user's geographical location information.

[0092] The real-time providing unit can analyze the user's social media activity and provide information during real-time provision. For example, the real-time providing unit prioritizes providing information about the area around a location where the user has checked in on social media. For example, the real-time providing unit analyzes the user's social media activity and prioritizes providing information about the area around the location where the user has checked in. The real-time providing unit can also prioritize providing information about stores that the user follows on social media. For example, the real-time providing unit analyzes the user's social media activity and prioritizes providing information about the stores that the user follows. Furthermore, the real-time providing unit can also prioritize providing information about events in which the user has shown interest on social media. For example, the real-time providing unit analyzes the user's social media activity and prioritizes providing information about events in which the user has shown interest. This makes it possible to provide optimal information based on the user's social media activity.

[0093] The update unit can estimate the user's emotions and adjust the update frequency of the coupon information based on the estimated user's emotions. For example, the update unit increases the update frequency of the coupon information when the user is relaxed. For example, the update unit estimates the user's emotions and determines that the user is relaxed, and increases the update frequency of the coupon information. The update unit can also decrease the update frequency of the coupon information when the user is stressed. For example, the update unit estimates the user's emotions and determines that the user is stressed, and decreases the update frequency of the coupon information. Furthermore, the update unit can also update the coupon information in real time when the user is in a hurry. For example, the update unit estimates the user's emotions and determines that the user is in a hurry, and updates the coupon information in real time. This makes it possible to adjust the update frequency of the coupon information according to the user's emotions.

[0094] The update unit can optimize the update algorithm by referring to past coupon usage data during updating. The update unit, for example, updates optimal coupon information based on the types of coupons used by the user in the past. For example, the update unit analyzes the user's past coupon usage data and updates optimal coupon information based on the types of coupons used. The update unit can also prioritize updating frequently used coupon information based on the user's past coupon usage history. For example, the update unit analyzes the user's past coupon usage history and prioritizes updating frequently used coupon information. Furthermore, the update unit can analyze the user's past coupon usage history and update the most effective coupon information. For example, the update unit analyzes the user's past coupon usage history and updates the most effective coupon information. This makes it possible to optimize the update algorithm based on past coupon usage data.

[0095] The update unit can estimate the user's emotions and determine the priority of coupon information based on the estimated user's emotions. For example, when the user is relaxed, the update unit prioritizes updating coupon information with a higher discount rate. For example, when the update unit estimates the user's emotions and determines that the user is relaxed, it prioritizes updating coupon information with a higher discount rate. Furthermore, when the user is in a hurry, the update unit can also prioritize updating coupon information that can be used immediately. For example, when the update unit estimates the user's emotions and determines that the user is in a hurry, it prioritizes updating coupon information that can be used immediately. Furthermore, when the user is excited, the update unit can also prioritize updating coupon information that offers many benefits. For example, when the update unit estimates the user's emotions and determines that the user is excited, it prioritizes updating coupon information that offers many benefits. In this way, the priority of coupon information can be determined according to the user's emotions.

[0096] When updating, the update unit can update information taking into consideration the expiration date and usage conditions of the coupon. For example, the update unit prioritizes updating coupon information that is close to its expiration date. For example, the update unit analyzes the expiration date of the coupon and prioritizes updating coupon information that is close to its expiration date. The update unit can also prioritize updating coupon information with simple usage conditions. For example, the update unit analyzes the usage conditions of the coupon and prioritizes updating coupon information with simple conditions. Furthermore, the update unit can also prioritize updating coupon information with usage conditions that are suitable for the user. For example, the update unit analyzes the usage conditions of the coupon and prioritizes updating coupon information that is suitable for the user. This makes it possible to update information based on the expiration date and usage conditions of the coupon. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, provision unit, selection unit, real-time provision unit, and update unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the user's current location using the GPS module of the smart device 14. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides store information and coupon information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically selects the most advantageous coupon at the time of payment. The real-time provision unit is realized, for example, by the control unit 46A of the smart device 14 and updates information whenever the user's location information changes. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and periodically updates coupon information. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, provision unit, selection unit, real-time provision unit, and update unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires the user's current location using the GPS module of the smart glasses 214. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides store information and coupon information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically selects the most advantageous coupon at the time of payment. The real-time provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and updates information whenever the user's location information changes. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and periodically updates coupon information. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, provision unit, selection unit, real-time provision unit, and update unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the user's current location using the GPS module of the headset-type terminal 314. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides store information and coupon information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically selects the most advantageous coupon at the time of payment. The real-time provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and updates information whenever the user's location information changes. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and periodically updates coupon information. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, provision unit, selection unit, real-time provision unit, and update unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the user's current location using the GPS module of the robot 414. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides store information and coupon information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically selects the most advantageous coupon at the time of payment. The real-time provision unit is realized, for example, by the control unit 46A of the robot 414 and updates information whenever the user's location information changes. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and periodically updates coupon information.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The acquisition unit can analyze the user's past movement history and adjust the frequency of acquiring location information during a specific time period. For example, the acquisition unit can identify places that the user has frequently visited in the past and increase the frequency of acquiring location information during times when the user is at those places. In addition, if the user tends to move during a specific time period, the acquisition unit can also increase the frequency of acquiring location information during that time period. Furthermore, if the user tends to stay in a specific place during a specific time period, the acquisition unit can also reduce the frequency of acquiring location information during that time period. This allows for efficient acquisition of location information based on the user's past movement history.

[0099] The providing unit can analyze the user's social media activity and provide related store information and coupon information. For example, the providing unit can prioritize providing coupon information for stores that the user follows on social media. The providing unit can also provide coupon information for products that the user has shown interest in on social media. Furthermore, the providing unit can also provide information about the surrounding area of ​​a location where the user has checked in on social media. This makes it possible to provide highly relevant information based on the user's social media activity.

[0100] The selection unit can estimate the user's emotions and adjust coupon selection criteria based on the estimated user's emotions. For example, when the user is relaxed, the selection unit can prioritize selecting coupons with higher discount rates. When the user is in a hurry, the selection unit can also prioritize selecting coupons that can be used immediately. Furthermore, when the user is excited, the selection unit can also prioritize selecting coupons with many benefits. In this way, the coupon selection criteria can be adjusted according to the user's emotions.

[0101] The real-time providing unit can analyze the user's current activity status and adjust the frequency of information provision. For example, the real-time providing unit can provide information frequently when the user is shopping. The real-time providing unit can also reduce the frequency of information provision when the user is eating at a restaurant. Furthermore, the real-time providing unit can also provide information in real time when the user is on the move. In this way, the frequency of information provision can be adjusted according to the user's current activity status.

[0102] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide information at a leisurely pace. If the user is in a hurry, the providing unit can also provide information quickly. Furthermore, if the user is excited, the providing unit can also provide information by adding visually stimulating effects. In this way, the method of providing information can be adjusted according to the user's emotions.

[0103] The update unit can estimate the user's emotions and adjust the update frequency of the coupon information based on the estimated user's emotions. For example, the update unit can increase the update frequency of the coupon information when the user is relaxed. Also, the update unit can decrease the update frequency of the coupon information when the user is stressed. Furthermore, the update unit can update the coupon information in real time when the user is in a hurry. In this way, the update frequency of the coupon information can be adjusted according to the user's emotions.

[0104] The acquisition unit can adjust the timing of acquiring location information based on the user's current activity status and area of ​​interest. For example, when the user is shopping, the acquisition unit prioritizes acquiring shopping-related location information. When the user is in a restaurant, the acquisition unit can also prioritize acquiring restaurant-related location information. Furthermore, when the user is in a tourist spot, the acquisition unit can also prioritize acquiring tourist spot-related location information. This makes it possible to acquire location information according to the user's current activity status and area of ​​interest.

[0105] The providing unit can adjust the level of detail of the provided information based on the importance of the store information and coupon information when providing the information. For example, the providing unit can provide coupon information with high importance in detail. The providing unit can also provide store information with low importance in a concise manner. Furthermore, the providing unit can provide information with medium importance in an appropriate level of detail. This makes it possible to adjust the level of detail of the provided information based on the importance of the store information and coupon information.

[0106] The real-time providing unit can estimate the user's emotions and determine the priority of real-time information based on the estimated user's emotions. For example, if the user is relaxed, the real-time providing unit can provide entertainment-related information with priority. Also, if the user is in a hurry, the real-time providing unit can provide traffic information or emergency information with priority. Furthermore, if the user is excited, the real-time providing unit can provide event information or special offer information with priority. In this way, the priority of real-time information can be determined according to the user's emotions.

[0107] When selecting a coupon, the selection unit can select an appropriate coupon by taking into consideration the coupon's expiration date and terms of use. For example, the selection unit can prioritize selecting coupons that are close to their expiration date. The selection unit can also prioritize selecting coupons with simple terms of use. Furthermore, the selection unit can also prioritize selecting coupons with terms of use that are suitable for the user. This allows the optimal coupon to be selected by taking into consideration the coupon's expiration date and terms of use.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The acquisition unit acquires the user's current location using GPS. For example, the acquisition unit acquires the current location using a GPS module installed in the user's smartphone. The acquisition unit can also periodically update the user's current location. For example, the acquisition unit acquires GPS data at regular intervals and updates the current location. Step 2: The providing unit uses the generation AI to provide store information and coupon information based on the location information acquired by the acquisition unit. For example, the generating AI collects store information around the user's current location and generates coupon information. The providing unit can also analyze the user's past behavior history and provide optimal coupon information. For example, the providing unit prioritizes providing coupon information for stores the user has visited in the past. Step 3: The selection unit automatically selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit. For example, the selection unit compares the discount rates and terms of use of coupons and selects the most advantageous coupon. The selection unit can also refer to the user's past coupon usage history to select the most appropriate coupon. For example, the selection unit selects the most appropriate coupon based on the types and frequency of coupons the user has used in the past. Step 4: The real-time providing unit updates the information provided by the providing unit in real time. For example, the real-time providing unit updates store information and coupon information every time the user's location information changes. The real-time providing unit can also adjust the frequency of information provision based on the user's current activity status and areas of interest. For example, the real-time providing unit provides information frequently when the user is shopping and reduces the frequency of information provision when the user is eating at a restaurant.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring GPS location information; a providing unit that provides store information or coupon information based on the location information acquired by the acquiring unit; a selection unit that selects the most advantageous coupon at the time of payment based on the coupon information provided by the provision unit; a real-time providing unit that updates the information provided by the providing unit in real time. A system characterized by:

2. The providing unit Providing store information and coupon information using generative AI The system of claim 1 .

3. The selection unit Automatically select the coupon with the highest discount rate at checkout The system of claim 1 .

4. The real-time providing unit Real-time updates based on the user's location The system of claim 1 .

5. The providing unit Analyze user history and provide appropriate coupon information The system of claim 1 .

6. Further provided is an update unit that periodically updates coupon information. The system of claim 1 .

7. The acquisition unit Estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions. The system of claim 1 .

8. The acquisition unit Analyze the user's past movement history and select the appropriate acquisition method The system of claim 1 .

9. The acquisition unit Filter location information based on the user's current activity and interests The system of claim 1 .

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